Gastos Casa
15 enero, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 1/12/2023 | Diosi | 168000 | Tami | Consulta vet + hospitalización + exámenes |
| 2/12/2023 | Comida | 83183 | Tami | Supermercado |
| 2/12/2023 | Farmacia | 36819 | Tami | Diolasa + Clotrimazol + Mulcatel + Propolgea |
| 7/12/2023 | Diosi | 30000 | Tami | Consulta Veterinaria |
| 8/12/2023 | Agua | 15080 | Andrés | NA |
| 9/12/2023 | Comida | 57905 | Tami | Supermercado |
| 9/12/2023 | Comida | 20614 | Tami | Fork pedido el 28/11 |
| 17/12/2023 | Comida | 40000 | Andrés | NA |
| 17/12/2023 | Comida | 8000 | Andrés | almuerzo |
| 17/12/2023 | VTR | 22000 | Andrés | NA |
| 17/12/2023 | Comida | 42000 | Andrés | piwen |
| 17/12/2023 | Comida | 48432 | Tami | Supermercado |
| 17/12/2023 | Enceres | 16400 | Tami | Incoludido |
| 19/12/2023 | Aporte Basureros | 10000 | Tami | NA |
| 22/12/2023 | Netflix | 8326 | Tami | NA |
| 22/12/2023 | Diosi | 14700 | Andrés | Antiparasitario |
| 24/12/2023 | Comida | 79633 | Tami | Supermercado |
| 24/12/2023 | Comida | 19950 | Andrés | Empanadas |
| 25/12/2023 | Uber | 5595 | Tami | NA |
| 27/12/2023 | Diosi | 22970 | Andrés | arena |
| 30/12/2023 | Comida | 50000 | Andrés | jumbo la litad de los 97 |
| 2/1/2024 | Electricidad | 55466 | Andrés | pac enel |
| 2/1/2024 | Comida | 44542 | Tami | Supermercado |
| 5/1/2024 | Comida | 9516 | Tami | Burger King pre Maite |
| 5/1/2024 | Comida | 19590 | Tami | Barritas Soul |
| 8/1/2024 | Comida | 57625 | Tami | Supermercado |
| 13/1/2024 | Limpieza alfombras | 60000 | Tami | NA |
| 13/1/2024 | Comida | 33110 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 8.5787e+08 2 8.0551 3e-04 ***
## lag_depvar 9.6956e+10 1 1820.7627 <2e-16 ***
## Residuals 3.4932e+10 656
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 904.3011 13553.38 0.020309
## 2-0 29635.497 23893.8216 35377.17 0.000000
## 2-1 22406.659 19026.0521 25787.27 0.000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
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## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
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## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
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## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
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## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 504 51869.76 15474.480
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1926.16446 4000.63980 -498.59715 2472.29262 -2891.57278 547.11832
## 8 9 10 11 12 13
## -5614.13268 -1234.79685 -4018.01934 -519.25218 -5026.53680 -1757.02422
## 14 15 16 17 18 19
## -1046.39929 243.21485 -3345.55472 -511.79427 -2244.73052 6477.87924
## 20 21 22 23 24 25
## -1523.93330 -1220.08272 1454.14205 -1172.95837 235.75917 1708.25751
## 26 27 28 29 30 31
## -7054.45133 882.39726 8159.44816 531.09810 100.06640 -2292.52927
## 32 33 34 35 36 37
## 1640.24356 4662.67653 1288.60431 2559.67804 -1673.50494 4756.19035
## 38 39 40 41 42 43
## 4391.20254 -2128.35730 -2888.88150 -1076.94434 -10729.73937 7125.86890
## 44 45 46 47 48 49
## 2533.44913 1388.27273 8146.79630 854.87320 6688.06947 6960.36318
## 50 51 52 53 54 55
## -5557.61913 -4606.46133 -4971.71978 -7933.08513 5997.84757 -4091.69253
## 56 57 58 59 60 61
## -4972.99612 3708.28449 822.20756 -74.36254 105.47416 -5025.54820
## 62 63 64 65 66 67
## 18020.90578 3843.17108 -3409.29177 6073.87318 7570.82537 14957.14298
## 68 69 70 71 72 73
## 2209.88950 -12732.83473 -1100.39440 4802.95655 -4684.74361 -4294.87494
## 74 75 76 77 78 79
## -10472.23157 2319.57388 -5487.49253 900.46194 -6990.82790 327.65218
## 80 81 82 83 84 85
## -2536.43557 -2890.07151 -4146.10835 -787.55678 2088.27127 3603.18539
## 86 87 88 89 90 91
## 399.20536 -544.03278 137.46370 4254.12460 -1134.40882 1157.70357
## 92 93 94 95 96 97
## -2039.13162 -1054.88371 152.12768 256.12935 -7495.17584 2261.62080
## 98 99 100 101 102 103
## -8676.69003 -3143.75590 -4263.61676 -1996.94765 -1515.80635 2939.16814
## 104 105 106 107 108 109
## -2501.00535 2418.04959 -1269.41154 855.49986 2503.10889 -3185.16673
## 110 111 112 113 114 115
## -4800.21969 -993.30501 1765.58437 11604.59875 -1130.37491 2747.23097
## 116 117 118 119 120 121
## 4375.56760 3670.95984 -895.40129 -4554.57929 -3658.24192 2317.89215
## 122 123 124 125 126 127
## -1695.92653 1345.30493 8885.02261 1014.66035 291.38755 -2377.70870
## 128 129 130 131 132 133
## 2740.52603 7170.97524 1230.88487 -8291.33800 1794.25468 4203.96742
## 134 135 136 137 138 139
## -3036.43947 -1358.73124 -822.86492 -3865.85162 1133.42919 -518.89953
## 140 141 142 143 144 145
## -2941.30347 1647.60132 -1914.16483 -7887.87807 1862.37438 -3600.70006
## 146 147 148 149 150 151
## 1940.94387 -363.69304 926.74475 -426.19092 1288.62734 1153.65660
## 152 153 154 155 156 157
## 3347.65570 -4814.58290 -1211.15349 -3286.13599 5861.12656 9760.19194
## 158 159 160 161 162 163
## -3630.28689 -5007.43002 3326.38615 10.17688 2537.60649 -6006.23086
## 164 165 166 167 168 169
## -6926.92157 3890.65868 17226.02267 3753.71232 -233.46316 -2310.22383
## 170 171 172 173 174 175
## -1026.29484 3638.95265 -131.43126 -7996.77224 2802.54741 4314.87252
## 176 177 178 179 180 181
## 681.99839 8807.03972 -9063.94114 -3458.90271 -10790.72853 -11452.32793
## 182 183 184 185 186 187
## 869.87499 8991.37462 -1550.36422 5797.97569 6530.31494 13233.78885
## 188 189 190 191 192 193
## 8695.08455 -3709.18746 2702.44264 10606.11180 -1284.40831 -2164.92906
## 194 195 196 197 198 199
## -10082.34493 -6356.41049 1134.89231 -5306.25815 -9945.95845 5096.83716
## 200 201 202 203 204 205
## -3239.75657 -1913.94521 -1010.95024 6296.72553 9802.45938 651.53103
## 206 207 208 209 210 211
## 2989.59183 3191.70471 5905.99839 13020.60777 -5336.08697 -11083.73389
## 212 213 214 215 216 217
## -5659.85216 -10673.80630 -5317.10075 1233.91892 -13247.03795 15980.57167
## 218 219 220 221 222 223
## 7697.23917 1542.54054 26717.24873 12947.18067 7882.16175 14605.50930
## 224 225 226 227 228 229
## -3205.95034 -1192.21980 4221.20247 796.77927 3125.78447 9371.55163
## 230 231 232 233 234 235
## 6282.74301 -1426.47649 -1443.39655 9728.11305 -11106.87302 -7114.47041
## 236 237 238 239 240 241
## -8510.66330 -10210.70694 2823.12973 1172.04940 -8438.52199 -9243.94141
## 242 243 244 245 246 247
## 8731.65617 -7948.77290 2204.57398 -10514.97773 -4401.54053 1053.25265
## 248 249 250 251 252 253
## 697.28370 -12572.75875 3227.75284 1749.69202 3964.25496 1976.05180
## 254 255 256 257 258 259
## -1273.48361 11016.38722 20932.96840 3543.55207 -3932.30543 4331.16846
## 260 261 262 263 264 265
## -1449.74833 3915.88060 -4649.56759 -10798.80888 -4816.85057 -677.03365
## 266 267 268 269 270 271
## -5339.33246 8561.13469 -4348.52238 4057.20025 -2169.81443 4335.22113
## 272 273 274 275 276 277
## 680.98479 7278.65877 -1337.03753 12058.38568 -4392.38174 1812.32591
## 278 279 280 281 282 283
## -283.72835 7911.05693 -4903.81494 -2682.20453 -11268.51933 -2846.87696
## 284 285 286 287 288 289
## 18453.24945 7866.08084 2905.67290 -452.45019 1038.09214 6514.06331
## 290 291 292 293 294 295
## 7062.29205 -18530.26607 -11204.20522 -8342.68142 9352.08086 2930.14058
## 296 297 298 299 300 301
## -1266.78472 27301.23306 10352.52110 5279.94603 9904.47690 3314.08370
## 302 303 304 305 306 307
## -606.37481 8241.21484 -23896.32397 -3533.37978 -229.32404 -7023.51532
## 308 309 310 311 312 313
## -4121.46832 2743.00478 -9320.28256 -3471.28959 -8442.28084 1229.19919
## 314 315 316 317 318 319
## -3423.41228 1766.30315 -4302.51779 27194.69247 -592.28155 3385.72176
## 320 321 322 323 324 325
## 10947.48213 5828.18655 32654.13923 5780.37352 -20294.89774 2057.67730
## 326 327 328 329 330 331
## 1357.46072 -6242.98302 -1635.86394 -33211.75104 512.75189 -2604.37769
## 332 333 334 335 336 337
## -380.17038 -3412.34391 3836.50831 -593.98911 -7090.25398 -3323.82461
## 338 339 340 341 342 343
## -2407.57635 -7890.50314 3572.41760 -1557.69718 -1913.14358 -1164.30609
## 344 345 346 347 348 349
## 20.29400 352.65897 -1720.38365 -9553.14506 -13423.91883 1954.49181
## 350 351 352 353 354 355
## -4596.54893 -3947.61654 -6273.71767 1422.51127 1126.67333 2550.56827
## 356 357 358 359 360 361
## -3904.78396 -684.09432 526.60048 6892.85324 262.74623 -46.94426
## 362 363 364 365 366 367
## 2574.78159 -2724.32786 -889.49517 -8763.59792 -4751.90089 -6376.21671
## 368 369 370 371 372 373
## -5165.74730 -7497.79124 4715.39675 178.36916 6960.11170 -7682.65267
## 374 375 376 377 378 379
## -2397.05231 -3528.72793 -2627.55633 -12623.13617 1606.41578 -10862.01041
## 380 381 382 383 384 385
## 5368.78966 9128.14788 3065.01733 -2412.00433 1563.14913 6728.33973
## 386 387 388 389 390 391
## 11480.14411 -5603.84692 -5271.46113 -151.07257 8563.13782 1917.20265
## 392 393 394 395 396 397
## 11326.93528 -9655.17808 2834.32232 792.41995 633.43734 -591.97274
## 398 399 400 401 402 403
## -524.16118 -14466.58227 8362.08621 -1211.41009 -1413.47272 6929.46304
## 404 405 406 407 408 409
## -7892.34241 -1359.26231 -2598.53661 -5904.26960 -2998.83151 -4067.27021
## 410 411 412 413 414 415
## -8927.26685 5883.57232 1504.18223 -7467.85539 -7860.06881 13986.93804
## 416 417 418 419 420 421
## 3787.57717 4510.95290 -7967.90150 -4783.20963 -2689.18799 2716.43799
## 422 423 424 425 426 427
## -14062.28914 -3002.28870 -9308.85408 2726.95580 6772.18833 6486.22161
## 428 429 430 431 432 433
## -3983.28235 -4163.34055 -4805.40223 -1914.56867 -5836.93576 -6799.59090
## 434 435 436 437 438 439
## -6174.71247 -1654.82424 -1085.07862 -5185.07511 2344.69554 4671.12189
## 440 441 442 443 444 445
## -5139.29026 -2287.34126 1443.02448 -3928.66944 2713.92150 -6644.25176
## 446 447 448 449 450 451
## -12243.82191 -4770.19706 9372.46258 -2142.31273 4639.78074 -5904.26539
## 452 453 454 455 456 457
## -1219.53935 292.30921 2957.86714 -12284.52429 3208.34956 -6792.11279
## 458 459 460 461 462 463
## 6366.34256 2964.75207 2508.77573 -3808.09379 2082.56165 15.09087
## 464 465 466 467 468 469
## 1817.22496 -473.16424 3390.09160 -2558.66992 5847.00324 -6828.26400
## 470 471 472 473 474 475
## -2945.84362 -2218.81567 -4695.11989 2919.10360 7775.32396 -5932.20841
## 476 477 478 479 480 481
## 1489.28457 -6148.67239 -2888.05187 1946.59456 -12953.37133 -9930.91796
## 482 483 484 485 486 487
## -1469.62261 -217.79333 -1159.68429 -1516.83010 -9745.11629 10842.95182
## 488 489 490 491 492 493
## 6165.28641 7445.56300 -5314.72332 5410.42954 9404.32305 6272.83987
## 494 495 496 497 498 499
## -13202.92934 -10495.75998 -3506.06891 -1196.98292 -608.58920 -7696.92219
## 500 501 502 503 504 505
## 462.31343 4176.17802 5475.42619 709.62863 139.44249 -7179.10810
## 506 507 508 509 510 511
## 538.35496 -5059.66756 1767.47099 -1320.55433 -8186.47255 -721.75180
## 512 513 514 515 516 517
## -2776.53700 -700.98141 1235.79841 -9552.75528 -7927.32856 24056.10386
## 518 519 520 521 522 523
## 9943.49851 6113.19496 -5052.83405 2981.01060 17218.62101 11867.11001
## 524 525 526 527 528 529
## -23659.31802 -4938.21767 -3685.92754 4572.56357 -291.84424 -11044.87891
## 530 531 532 533 534 535
## 4307.41667 13900.03923 -4802.41202 4466.53949 5695.89576 -1593.41939
## 536 537 538 539 540 541
## -4388.39058 -6993.90311 -2113.92055 8292.00219 213.67874 -8058.42893
## 542 543 544 545 546 547
## 1791.64157 -589.92805 374.53461 -11011.52037 -11177.07256 1808.94721
## 548 549 550 551 552 553
## 6837.90678 -1363.31126 788.88201 -7743.36210 8455.58468 929.32275
## 554 555 556 557 558 559
## -11905.51658 9051.45030 8685.38786 238.79918 4980.86265 -3396.89107
## 560 561 562 563 564 565
## 14221.92612 21780.33714 -5947.87993 -9311.51309 6975.61198 489.75350
## 566 567 568 569 570 571
## 3689.62154 -7125.82685 -17178.25284 6564.28545 6443.32972 1999.79708
## 572 573 574 575 576 577
## 3214.57531 1917.57694 -2007.24822 14833.53589 -9354.97341 -6107.70183
## 578 579 580 581 582 583
## 8756.93324 3015.61070 -6366.68565 7587.15238 -3629.47692 -2669.51386
## 584 585 586 587 588 589
## 15766.64179 -14237.02376 8476.29440 226.88885 -6068.61210 -700.67050
## 590 591 592 593 594 595
## 297.50748 -10601.64430 1715.01156 -7182.28137 2953.75488 8817.19586
## 596 597 598 599 600 601
## -7420.50013 5833.86053 2803.65941 6958.92342 -3004.80592 6277.59221
## 602 603 604 605 606 607
## -8100.25867 2329.54051 1372.52722 3252.63097 1643.68837 554.39315
## 608 609 610 611 612 613
## -5660.17067 8140.11246 -1013.50502 -2431.96900 -3352.80149 -8174.66383
## 614 615 616 617 618 619
## 11910.53197 5060.26438 -9132.99554 11674.02936 6252.41196 -5308.78705
## 620 621 622 623 624 625
## 26527.71089 -12325.22497 -6500.28979 3323.35642 -3962.33671 -10463.96546
## 626 627 628 629 630 631
## 11281.69547 -21488.57963 -2559.48058 8531.02485 11137.23322 -1392.67340
## 632 633 634 635 636 637
## 33414.48338 -6024.82311 6129.24634 5842.93426 -1801.49660 -4958.59695
## 638 639 640 641 642 643
## -1663.53251 -12204.94724 -2198.39900 -1865.33857 -2514.56586 -2873.64971
## 644 645 646 647 648 649
## 1776.50376 4434.21562 17038.77886 18853.07799 1399.00419 5257.72872
## 650 651 652 653 654 655
## 11091.74201 20717.98307 1518.67240 -27368.44572 -1098.07886 -2082.82064
## 656 657 658 659 660 661
## 2034.36414 -3006.44255 -10487.22038 1654.49998 4256.39308 -907.30751
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17343.12 20138.36 24314.74 24037.85 26348.29 23729.60 24432.85 19751.94
## 10 11 12 13 14 15 16 17
## 19493.31 16884.54 17647.82 14436.88 14487.11 15139.64 16805.27 15155.94
## 18 19 20 21 22 23 24 25
## 16171.73 15556.69 22509.93 21610.65 21100.00 22955.53 22293.81 22934.46
## 26 27 28 29 30 31 32 33
## 24746.74 18785.89 20480.55 28174.90 28231.51 27910.39 25583.04 26959.89
## 34 35 36 37 38 39 40 41
## 30732.82 31074.89 32458.36 30014.38 34051.80 37201.36 34311.17 31180.23
## 42 43 44 45 46 47 48 49
## 30049.03 20800.42 28181.98 30574.01 31643.35 38356.70 37860.50 42437.64
## 50 51 52 53 54 55 56 57
## 46596.62 39427.75 34095.29 29208.80 22478.30 28653.55 25296.57 21661.72
## 58 59 60 61 62 63 64 65
## 25989.65 27226.22 27517.81 27922.12 23868.38 40156.97 41967.29 37299.98
## 66 67 68 69 70 71 72 73
## 41430.17 46256.14 56729.68 54779.69 40292.11 37843.47 40806.32 35210.45
## 74 75 76 77 78 79 80 81
## 30745.66 21618.71 24761.78 20761.82 22809.83 17798.49 19777.15 19017.79
## 82 83 84 85 86 87 88 89
## 18063.25 16167.41 17421.87 20964.10 25301.22 26273.03 26297.54 26903.02
## 90 91 92 93 94 95 96 97
## 30952.84 29804.73 30785.85 28885.60 28100.02 28461.44 28860.60 22555.24
## 98 99 100 101 102 103 104 105
## 25515.26 18672.90 17549.90 15626.38 15920.66 16585.69 20976.72 20076.95
## 106 107 108 109 110 111 112 113
## 23523.98 23317.79 24963.32 27787.60 25331.36 21839.73 22110.13 24708.12
## 114 115 116 117 118 119 120 121
## 35374.37 33600.20 35404.15 38347.75 40267.97 37998.58 32914.10 29322.25
## 122 123 124 125 126 127 128 129
## 31367.07 29678.41 30838.41 38299.48 37948.47 37027.14 33947.90 35696.60
## 130 131 132 133 134 135 136 137
## 40995.97 40446.48 31808.75 33050.46 36182.01 32658.16 31074.86 30176.57
## 138 139 140 141 142 143 144 145
## 26796.43 28185.04 27958.87 25687.40 27674.88 26324.74 20043.63 23018.84
## 146 147 148 149 150 151 152 153
## 20885.20 23807.98 24338.11 25899.48 26078.23 27702.20 28979.20 31956.01
## 154 155 156 157 158 159 160 161
## 27508.87 26785.28 24385.16 30171.67 41650.72 40011.43 37424.47 42353.11
## 162 163 164 165 166 167 168 169
## 43735.96 47089.52 42638.21 38031.06 43357.26 59361.86 61533.61 59976.65
## 170 171 172 173 174 175 176 177
## 56860.29 55288.76 57942.00 56983.92 49416.74 52188.70 55863.00 55898.53
## 178 179 180 181 182 183 184 185
## 62897.23 53572.90 50383.16 41359.61 33053.41 36497.63 46416.65 45882.60
## 186 187 188 189 190 191 192 193
## 51726.69 57366.78 67952.92 73139.33 66949.13 67139.03 74080.27 69835.64
## 194 195 196 197 198 199 200 201
## 65440.20 54880.41 49019.54 50417.83 46092.96 38404.73 44712.19 42971.95
## 202 203 204 205 206 207 208 209
## 42616.52 43086.13 49756.11 58483.04 58119.41 59812.72 61438.29 65160.25
## 210 211 212 213 214 215 216 217
## 74453.94 66681.31 55086.00 49793.23 40953.96 37967.22 41024.04 31226.43
## 218 219 220 221 222 223 224 225
## 47890.05 55077.17 55962.61 78312.39 85670.55 87637.20 95089.95 86206.08
## 226 227 228 229 230 231 232 233
## 80314.08 79903.65 76614.79 75791.59 80442.11 81781.48 76318.54 71618.89
## 234 235 236 237 238 239 240 241
## 77169.30 64060.90 56242.81 48340.42 40105.16 44220.52 46333.95 39904.23
## 242 243 244 245 246 247 248 249
## 33699.20 43793.92 38145.85 42009.69 34414.83 33144.32 36732.86 39505.19
## 250 251 252 253 254 255 256 257
## 30502.10 36331.74 40063.75 45163.66 47832.34 47334.18 57447.03 74624.73
## 258 259 260 261 262 263 264 265
## 74443.16 67875.97 69330.75 65620.55 67040.28 60911.95 50382.42 46482.32
## 266 267 268 269 270 271 272 273
## 46687.90 42865.72 51509.09 47850.23 51921.24 50072.21 54065.30 54355.91
## 274 275 276 277 278 279 280 281
## 60263.47 57940.90 67437.24 61472.96 61679.16 60058.37 65696.39 59541.35
## 282 283 284 285 286 287 288 289
## 56167.95 45911.02 44337.04 61254.63 66683.76 67085.74 64550.48 63654.51
## 290 291 292 293 294 295 296 297
## 67582.42 71421.27 52764.78 43047.54 37167.92 47300.86 50483.50 49613.62
## 298 299 300 301 302 303 304 305
## 73368.19 79205.05 79860.52 84388.77 82620.23 77741.21 81144.75 56501.81
## 306 307 308 309 310 311 312 313
## 52831.18 52516.80 46420.33 43680.71 47218.28 39906.43 38651.85 33312.66
## 314 315 316 317 318 319 320 321
## 37028.13 36224.41 39985.95 38007.16 63322.85 61203.42 62797.38 70649.53
## 322 323 324 325 326 327 328 329
## 72993.29 98009.91 96417.18 72688.47 71508.25 69895.55 61994.15 59168.89
## 330 331 332 333 334 335 336 337
## 29665.68 33285.95 33717.46 35995.06 35347.92 41009.70 42065.68 37399.97
## 338 339 340 341 342 343 344 345
## 36628.72 36753.07 32157.44 38046.98 38698.29 38952.02 39811.85 41565.20
## 346 347 348 349 350 351 352 353
## 43353.96 43110.15 36183.49 26923.37 32170.55 31052.33 30649.86 28309.77
## 354 355 356 357 358 359 360 361
## 32903.33 36589.15 40971.36 39193.38 40430.69 42530.15 49790.54 50331.09
## 362 363 364 365 366 367 368 369
## 50529.08 52947.33 50476.64 49931.31 42710.62 39958.50 36205.18 34024.36
## 370 371 372 373 374 375 376 377
## 30154.03 37309.06 39554.32 47296.08 41377.62 40834.87 39398.84 38940.14
## 378 379 380 381 382 383 384 385
## 29974.30 34488.58 27666.92 35736.42 45881.13 49381.58 47686.42 49641.80
## 386 387 388 389 390 391 392 393
## 55748.57 65061.13 58396.18 52965.22 52698.86 59943.94 60457.78 68968.46
## 394 395 396 397 398 399 400 401
## 58272.68 59811.01 59379.13 58872.40 57386.88 56171.01 43170.91 51600.12
## 402 403 404 405 406 407 408 409
## 50618.76 49603.82 55888.49 48566.83 47890.54 46247.70 42003.69 40855.70
## 410 411 412 413 414 415 416 417
## 38954.84 33156.57 40885.96 43759.00 38528.35 33706.06 48306.85 52081.62
## 418 419 420 421 422 423 424 425
## 55939.33 48545.64 44935.90 43635.99 47157.15 35787.15 35521.28 29884.62
## 426 427 428 429 430 431 432 433
## 35372.67 43548.64 50315.28 47139.63 44261.69 41242.85 41133.08 37675.02
## 434 435 436 437 438 439 440 441
## 33883.71 31168.11 32715.51 34531.22 32572.16 37349.74 43442.29 40253.77
## 442 443 444 445 446 447 448 449
## 39965.12 42916.81 40841.36 44758.25 40091.68 31287.20 30145.82 41296.03
## 450 451 452 453 454 455 456 457
## 40983.36 46531.69 42247.25 42590.55 44181.56 47832.10 37890.65 42651.68
## 458 459 460 461 462 463 464 465
## 38158.23 45589.53 49045.51 51618.38 48407.44 50705.62 50903.49 52618.74
## 466 467 468 469 470 471 472 473
## 52125.48 55015.67 52392.57 57351.84 50734.42 48388.82 47000.69 43686.47
## 474 475 476 477 478 479 480 481
## 47374.25 54701.78 49230.14 50902.39 45786.05 44194.55 46975.94 36582.78
## 482 483 484 485 486 487 488 489
## 30261.48 32096.79 34744.40 36207.26 37155.54 30912.05 43214.29 49753.29
## 490 491 492 493 494 495 496 497
## 56459.29 51267.00 56012.11 63506.87 67248.93 53755.33 44504.64 42565.55
## 498 499 500 501 502 503 504 505
## 42882.87 43659.64 38246.69 40601.96 45807.00 51385.23 52081.99 52190.54
## 506 507 508 509 510 511 512 513
## 46007.07 47322.67 43649.96 46355.27 46027.04 39857.18 40967.68 40157.84
## 514 515 516 517 518 519 520 521
## 41243.34 43835.33 36805.76 32171.04 55625.93 63638.09 67224.55 60724.13
## 522 523 524 525 526 527 528 529
## 62039.24 75377.60 82227.32 57633.50 52596.93 49351.44 53650.70 53166.02
## 530 531 532 533 534 535 536 537
## 43528.30 48429.25 60859.27 55479.89 58815.68 62730.85 59837.10 54958.33
## 538 539 540 541 542 543 544 545
## 48539.63 47220.00 55012.61 54767.57 47463.07 49646.21 49476.04 50157.23
## 546 547 548 549 550 551 552 553
## 40976.50 32960.91 37223.66 45192.45 44993.12 46667.93 40786.84 49635.68
## 554 555 556 557 558 559 560 561
## 50769.95 40735.26 50102.47 57822.06 57198.57 60730.75 56575.07 68121.38
## 562 563 564 565 566 567 568 569
## 84506.02 74777.51 63549.39 67888.10 66046.66 67211.68 58935.25 43216.00
## 570 571 572 573 574 575 576 577
## 50096.96 55894.49 57055.71 59093.42 59728.68 56907.46 68930.97 58497.99
## 578 579 580 581 582 583 584 585
## 52335.35 59798.39 61274.97 54494.85 60647.19 56303.94 53402.36 66725.17
## 586 587 588 589 590 591 592 593
## 52419.28 59629.68 58738.61 52575.24 51893.06 52164.07 43049.13 45795.00
## 594 595 596 597 598 599 600 601
## 40519.39 44687.80 53291.36 46744.14 52496.34 54830.79 60396.52 56624.69
## 602 603 604 605 606 607 608 609
## 61350.69 53073.03 54918.76 55680.94 57947.03 58510.61 58059.74 52343.32
## 610 611 612 613 614 615 616 617
## 59276.22 57371.68 54521.80 51287.95 44379.18 55679.59 59496.14 50596.83
## 618 619 620 621 622 623 624 625
## 60809.16 64917.79 58526.29 80348.51 65742.58 58211.79 60178.19 55616.25
## 626 627 628 629 630 631 632 633
## 46127.88 56640.01 37550.91 37413.69 46807.48 57098.96 55179.23 83384.25
## 634 635 636 637 638 639 640 641
## 73749.47 75910.07 77517.50 72340.03 65192.10 61887.80 50013.40 48411.48
## 642 643 644 645 646 647 648 649
## 47323.28 45833.22 44247.35 46875.36 51408.51 66106.21 80267.28 77443.13
## 650 651 652 653 654 655 656 657
## 78330.40 84094.73 97294.04 92148.30 62960.94 60459.25 57469.21 58435.87
## 658 659 660 661
## 54941.79 45529.50 47870.32 52109.31
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8266
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 8.055078 0.5162284 3.55956
## t2* 1820.762702 23.2706832 221.04557
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 3.484781 8.15329 14.96357
## 2 lag_depvar 1503.679097 1830.14658 2224.96432Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Jan 15 01:45:05 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jan 15 01:45:12 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jan 15 01:45:18 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jan 15 01:45:25 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jan 15 01:45:32 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jan 15 01:45:38 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jan 15 01:45:45 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jan 15 01:45:52 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Jan 15 01:45:58 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jan 15 01:46:05 2024
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 5.195333 | 5.410333 | 5.629750 | 6.5981458 |
| Comida | 366.009167 | 310.278417 | 314.087500 | 346.7794583 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 38.104750 | 47.072333 | 38.297667 | 33.8261667 |
| Enceres | 18.259750 | 20.086417 | 17.443792 | 23.0398333 |
| Farmacia | 4.733250 | 1.831667 | 7.913875 | 8.6494375 |
| Gas/Bencina | 35.219333 | 44.325000 | 28.954333 | 27.5965833 |
| Diosi | 55.804250 | 31.180667 | 41.934250 | 44.1985208 |
| donaciones/regalos | 0.000000 | 0.000000 | 7.170083 | 5.7233125 |
| Electrodomésticos/ Mantención casa | 0.000000 | 3.944000 | 30.269500 | 17.2805833 |
| VTR | 12.829167 | 25.156667 | 22.121792 | 19.0466250 |
| Netflix | 4.555500 | 7.151583 | 7.090167 | 6.7457708 |
| Otros | 0.000000 | 3.151083 | 1.575542 | 0.7877708 |
| Total | 540.710500 | 499.588167 | 522.488250 | 540.2722083 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2191, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-12-31 00:04:58 sería de: 37.564 pesos// Percentil 95% más alto proyectado: 40.778,21
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36833.92 | 36828.51 |
| Lo.80 | 36883.04 | 36886.39 |
| Point.Forecast | 37564.03 | 39253.82 |
| Hi.80 | 39365.36 | 43947.75 |
| Hi.95 | 40353.63 | 46432.57 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,0,0) with non-zero mean
##
## Coefficients:
## mean
## 1020.0204
## s.e. 22.4275
##
## sigma^2 = 30188: log likelihood = -387.51
## AIC=779.02 AICc=779.24 BIC=783.18paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2362 835.0084 6.1375
## s.e. 0.1436 294.8750 9.4651
##
## sigma^2 = 27668: log likelihood = -377.92
## AIC=763.85 AICc=764.6 BIC=772.09forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 747.5655 | 679.4817 | 743.9676 |
| Lo.80 | 863.5164 | 797.3541 | 832.2183 |
| Point.Forecast | 1082.5528 | 1020.0204 | 1028.4857 |
| Hi.80 | 1301.5893 | 1242.6866 | 1313.6949 |
| Hi.95 | 1417.6546 | 1360.5590 | 1495.4148 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 66 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.14 scales_1.3.0 ggiraph_0.8.8
## [7] tidytext_0.4.1 DT_0.31 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.4 xts_0.13.1
## [13] forecast_8.21.1 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.1 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.3 forcats_1.0.0
## [22] dplyr_1.1.4 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.4 tidyverse_2.0.0
## [28] sjPlot_2.8.15 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-4 sparklyr_1.8.4 httr_1.4.7
## [34] readxl_1.4.3 zoo_1.8-12 stringr_1.5.1
## [37] stringi_1.8.3 data.table_1.14.10 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 lazyeval_0.2.2 splines_4.1.2
## [7] crosstalk_1.2.0 digest_0.6.31 htmltools_0.5.5
## [10] fansi_1.0.4 ggfortify_0.4.16 magrittr_2.0.3
## [13] tzdb_0.4.0 modelr_0.1.11 vroom_1.6.5
## [16] askpass_1.1 timechange_0.2.0 anytime_0.3.9
## [19] tseries_0.10-55 colorspace_2.1-0 xfun_0.39
## [22] crayon_1.5.2 jsonlite_1.8.4 lme4_1.1-35.1
## [25] glue_1.6.2 gtable_0.3.4 emmeans_1.9.0
## [28] sjstats_0.18.2 sjmisc_2.8.9 car_3.1-2
## [31] quantmod_0.4.25 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.1 ggeffects_1.3.4 Rcpp_1.0.10
## [37] viridisLite_0.4.2 xtable_1.8-4 performance_0.10.8
## [40] bit_4.0.5 htmlwidgets_1.6.2 timeSeries_4032.109
## [43] gplots_3.1.3 ellipsis_0.3.2 spatial_7.3-14
## [46] pkgconfig_2.0.3 farver_2.1.1 nnet_7.3-16
## [49] sass_0.4.5 dbplyr_2.4.0 janitor_2.2.0
## [52] utf8_1.2.3 tidyselect_1.2.0 labeling_0.4.3
## [55] rlang_1.1.3 munsell_0.5.0 cellranger_1.1.0
## [58] tools_4.1.2 cachem_1.0.7 cli_3.6.1
## [61] generics_0.1.3 sjlabelled_1.2.0 broom_1.0.5
## [64] evaluate_0.20 fastmap_1.1.1 yaml_2.3.7
## [67] knitr_1.45 bit64_4.0.5 caTools_1.18.2
## [70] nlme_3.1-153 slam_0.1-50 xml2_1.3.3
## [73] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.14
## [76] curl_5.2.0 bslib_0.4.2 highr_0.10
## [79] fBasics_4032.96 Matrix_1.6-5 its.analysis_1.6.0
## [82] nloptr_2.0.3 urca_1.3-3 vctrs_0.6.5
## [85] pillar_1.9.0 lifecycle_1.0.3 lmtest_0.9-40
## [88] jquerylib_0.1.4 estimability_1.4.1 bitops_1.0-7
## [91] insight_0.19.7 R6_2.5.1 KernSmooth_2.23-20
## [94] janeaustenr_1.0.0 codetools_0.2-18 assertthat_0.2.1
## [97] boot_1.3-28 MASS_7.3-54 gtools_3.9.5
## [100] openssl_2.0.6 withr_2.5.2 fracdiff_1.5-2
## [103] bayestestR_0.13.1 parallel_4.1.2 hms_1.1.3
## [106] quadprog_1.5-8 timeDate_4032.109 minqa_1.2.6
## [109] snakecase_0.11.1 rmarkdown_2.25 carData_3.0-5
## [112] TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))